Method and circuit for updating a tap coefficient of a channel equalizer

ABSTRACT

Provided are a method of updating a tap coefficient of a channel equalizer while reducing the number of calculations and the divergence, and a circuit arranged and configured to execute the method. The method includes evaluating whether or not an error of the channel equalizer converges within a range of a threshold of visibility and determining the status of a control signal to select whether the tap coefficient of the channel equalizer will be updated using a least mean square (LMS) algorithm or a Kalman algorithm, wherein the LMS algorithm is the default error correction means and the Kalman algorithm is utilized when the control signal indicates the presence of a training signal.

[0001] This application claims priority under 35 U.S.C. § 119 fromKorean Patent Application No. 2003-4023, filed Jan. 21, 2003, in theKorean Intellectual Property Office, the disclosure of which isincorporated herein in its entirety by reference.

BACKGROUND OF THE INVENTION

[0002] 1. Field of the Invention

[0003] The present invention relates to a method of updatingcoefficients in a channel equalizer and a coefficient updating circuit,and more particularly, to a method of updating coefficients in a channelequalizer using either the Kalman algorithm or the least mean square(LMS) algorithm, and a circuit that may be used to perform the method.

[0004] 2. Description of the Related Art

[0005] Channel equalization is a technique of processing a signal, suchas a signal used in digital communication systems, to improve theperformance by reducing channel noise, channel distortion, multi-pathinterference and multi-user interference. Channel equalizers are usedmainly in household appliances such as digital TVs and personalcommunication systems in order to increase the ratio of an input signalrelative to interference and thereby reduce the symbol error rate of theinput signal.

[0006] Advanced Television Systems Committee (ATSC) provides standardsfor digital high-definition television (HD TV). ATSC document A53B ofAug. 7, 2001, describes approved standards for digital TV and ATSCdocument A54, Oct. 4, 1995, provides guidelines for the use of thesestandards. The standards specify specific training sequences that may beincorporated into video signals transmitted by terrestrial broadcast,cable or satellite channel. ATSC document A54 also discloses a methodfor adapting the filtering response of an equalizer to adequatelycompensate for channel distortion. This method does not, however, fullyaccount for the higher probability that coefficients for the equalizerare not set at levels sufficient to adequately compensate for channeldistortion when the equalizer first operates.

[0007] In order to force the convergence of the equalizer'scoefficients, a training sequence may be transmitted to and processed bythe adaptive equalizer to generate an output signal. This output signalmay then be compared with a locally generated or stored version of theexpected output signal to generate an error signal. The equalizercoefficients are then adjusted to minimize the value of the errorsignal, thereby improving the ability of the equalizer to filter aninput signal.

[0008] A linear filter is typically used for equalizing a channel, but afeedback-type non-linear filter may also be used to remove impulse noiseand non-linear distortion occurring in a communication channel andfurther improve the performance of the equalizer.

[0009] The conventional least mean square (LMS) algorithm, which is bothrelatively simple to implement and requires a relatively small amount ofcalculation, may be used as an algorithm for updating a tap coefficientof the equalizer. However, although the coefficients may be calculatedwith a small amount of calculation when using the LMS algorithm, theconvergence of the coefficients is relatively slow. Thus, the LMSalgorithm is generally unsuitable for a multi-path communicationenvironment in which the speed of and a delay in transmission of dataincrease.

[0010] The Kalman algorithm is a representative algorithm havingrelatively fast convergence characteristics. The Kalman algorithmhowever, presents application difficulties because it requires a largeamount of calculation. Although advances in hardware have enabled thewider use of the Kalman algorithm, the large amount of calculation anddivergence of coefficients remain problematic for applications of theKalman algorithm.

SUMMARY OF THE INVENTION

[0011] The exemplary embodiments of the present invention provide amethod for updating a tap coefficient for a channel equalizer, whilereducing the amount of calculation and reducing the likelihood ofdiverging coefficients and an embodiment circuit therefore performingthe method. The method includes determining whether or not an error ofthe channel equalizer converges within a range of a threshold ofvisibility and updating the tap coefficient of the channel equalizerusing 1) the least mean square (LMS) algorithm when the error convergeswithin the range of the threshold of visibility or 2) using either theLMS algorithm or the Kalman algorithm in response to a control signal.When determining the convergence of the error, the square of the errorof the channel equalizer is typically compared with the threshold ofvisibility.

[0012] When the updating the tap coefficient of the channel equalizer,the Kalman algorithm is typically used when the control signal is atraining signal and the LMS algorithm is typically used for othersignals. The error may be the difference between the training signal anda signal output from the channel equalizer in response to the trainingsignal or may be the difference between the signal output from thechannel equalizer and a signal output from a determination circuit wherethe determination circuit determines the signal output from the channelequalizer as a certain value.

[0013] Exemplary embodiments of the present invention provide a circuituseful for updating a tap coefficient for a channel equalizer comprisinga convergence examining and comparing unit (CEC unit), which determineswhether or not a received error of the channel equalizer convergeswithin the range of a threshold of visibility, and an updating circuitfor updating the tap coefficient using the LMS algorithm when the errorconverges within the range of the threshold of visibility and usingeither the LMS algorithm or the Kalman algorithm in response to acontrol signal. The updating circuit typically updates the tapcoefficient of the channel equalizer using the Kalman algorithm when thecontrol signal is a training signal and using the LMS algorithm inresponse to other signals.

[0014] When the updating circuit updates the tap coefficient of thechannel equalizer using the LMS algorithm, the tap coefficient isupdated according to Equation I:

c(n)=c(n−1)+μe(n)y(n)  (I)

[0015] wherein c(n) denotes an updated tap coefficient vector of thechannel equalizer, c(n-1) denotes a tap coefficient vector of thechannel equalizer that is yet to be updated, μ denotes the step size,e(n) denotes an error of the channel equalizer and y(n) denotes datainput to the channel equalizer.

[0016] When the tap coefficient of the channel equalizer is updatedusing the Kalman algorithm, the coefficient is updated according toEquation II:

c(n)=c(n−1)+K(n)e(n)  (II)

[0017] wherein c(n) denotes an updated tap coefficient vector of thechannel equalizer, c(n-1) denotes a tap coefficient vector of thechannel equalizer that is yet to be updated, K(n) denotes a Kalman gainvector, and e(n) denotes an error of the channel equalizer.

[0018] The exemplary embodiments of the present invention also provide acircuit for updating a tap coefficient of a channel equalizer, includinga slicer, which determines a signal output from the channel equalizer asa certain value; a selection circuit, which outputs a signal output fromthe slicer or a training signal as a signal output from the channelequalizer, in response to a control signal, the channel equalizer havingan updated tap coefficient; a subtracter which subtracts the signaloutput from the channel equalizer from the signal output from theselection circuit; a CEC unit which compares a threshold of visibilitywith a signal output from the subtracter and outputs the comparisonresult; a decoder which decodes the control signal and the comparisonresult output from the CEC unit and outputs the decoding result; and anupdating circuit which updates the tap coefficient of the channelequalizer in response to a signal output from the decoder. The updatingcircuit updates the tap coefficient of the channel equalizer using theLMS algorithm when an error of the channel equalizer converges withinthe range of the threshold of visibility or, when the error of thechannel equalizer does not converge within the range of the threshold ofvisibility and the control signal is a training signal using the Kalmanalgorithm and updates the tap coefficient using the LMS algorithm whenthe control signal is not the training signal.

BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The method and circuits comprising the present invention willbecome more apparent by describing in detail exemplary embodimentsthereof with reference to the attached drawings in which:

[0020]FIG. 1 illustrates the memory structure of a conventional errorcovariance matrix;

[0021]FIG. 2 illustrates the memory structure of an error covariancematrix according to an exemplary embodiment of the present invention;

[0022]FIG. 3 is a block diagram of a channel equalizer according to anexemplary embodiment of the present invention; and

[0023]FIG. 4 is a flowchart illustrating a method of updating acoefficient of a channel equalizer, according to an exemplary embodimentof the present invention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

[0024] The present invention will now be described more fully withreference to the accompanying drawings, in which exemplary embodimentsof the invention are shown. The same reference numerals, if used indifferent drawings, are intended to represent the same or correspondingelements, and their descriptions will not, therefore, be repeated.

[0025] The least mean square (LMS) algorithm requires a small amount ofcalculation, and stable performance, but has slow convergencecharacteristics. An error e(n) and an updated coefficient c(n) obtainedwhen applying the LMS algorithm to a channel equalizer can be expressedby the Equations III:

e(n)=s*(n)−y ^(*T)(n)c(n−1)

c(n)=c(n−1)+∥r(n)y(n)  (III)

[0026] wherein e(n) denotes the difference, i.e., the error, between atraining signal, which is generated at a time n by the channel equalizerand a signal which passes through a filtering circuit of the channelequalizer. s*(n) denotes an output of the channel equalizer having anupdated coefficient, i.e., a value of an equalized output. y^(*T)denotes data that is input to the channel equalizer and is equivalent toy^(T), y* denotes a conjugate complex number and y^(T) denotes atransformation matrix. c(n) denotes a tap coefficient vector at a timen; c(n−1) denotes a tap coefficient vector of the channel equalizer thathas yet to be updated; μ denotes the size of a step and y(n) denotesdata input to the channel equalizer. When updating a tap coefficient ofthe channel equalizer using the LMS algorithm, the amount of calculationrequired is N, N being proportional to the number of taps.

[0027] The Kalman algorithm has high-speed convergence characteristics,but requires a large amount of calculation and a large memory capacity,thus increasing the time required to perform the calculations andlikelihood of divergence. For instance, when applying the Kalmanalgorithm to an 8-vestigial side band (VSB) system, the Kalman algorithmguarantees convergence for a short training time in a multi-path channelenvironment but requires a large amount of calculation and a largememory capacity.

[0028] An error e(n) and an updated tap coefficient c(n) obtained whenapplying the Kalman algorithm to a channel equalizer can be expressed bythe Equations IV: $\begin{matrix}{{{k(n)} = \frac{{\phi^{- 1}\left( {n - 1} \right)}{y(n)}}{1 + {{y^{*T}(n)}{\phi^{- 1}\left( {n - 1} \right)}{y(n)}}}}{{e(n)} = {{s*(n)} - {{y^{*T}(n)}{c\left( {n - 1} \right)}}}}{{c(n)} = {{c\left( {n - 1} \right)} + {{K(n)}{e(n)}}}}{\phi^{- 1} = {{\phi^{- 1}\left( {n - 1} \right)} - {{K(n)}y^{*T}{\phi^{- 1}\left( {n - 1} \right)}}}}} & ({IV})\end{matrix}$

[0029] wherein K(n) denotes a Kalman gain vector, φ⁻¹(n) denotes anerror covariance matrix at a time n, and φ⁻¹(n−1) denotes an errorcovariance matrix a time n−1 prior to time n. When updating the tapcoefficient of the channel equalizer using the Kalman algorithm, theamount of calculation required is N² with N being proportional to thenumber of taps.

[0030] Assuming that the formula commonly expressed in the Kalman gainvector of Equation IV is J, a transformation formula J^(T) of theformula J can be expressed by the Equations V:

J=φ⁻¹(n−1)y(n)

J ^(T) =[y ^(*T)φ⁻¹(n−1)]  (V)

[0031] The Kalman algorithm which can be applied to the channelequalizer according to the present invention can be simplified usingEquation V, as shown by Equations VI: $\begin{matrix}{{J = {{\phi^{- 1}\left( {n - 1} \right)}{y(n)}}}{{K(n)} = \frac{J}{1 + {{y^{*T}(n)}J}}}{{e(n)} = {{s*(n)} - {{y^{*T}(n)}{c\left( {n - 1} \right)}}}}{{c(n)} = {{c\left( {n - 1} \right)} + {{K(n)}{e(n)}}}}{{\phi^{- 1}(n)} = {{\phi^{- 1}\left( {n - 1} \right)} - {{K(n)}y^{*T}J^{T}}}}} & ({VI})\end{matrix}$

[0032] The amount of calculation of a channel equalizer using theconventional Kalman algorithm of Equations IV is 3N² when the amount ofcalculation of φ⁻¹(n−1)y(n) is N², whereas the amount of calculation ofthe channel equalizer using the Kalman algorithm of Equation VI,according to an exemplary embodiment of the present invention, will beN² because J is replaced once with J^(T). Therefore, the amount ofcalculation of the channel equalizer using the Kalman algorithmaccording to the exemplary embodiment of the present invention can bereduced by about two thirds.

[0033]FIG. 1 illustrates the memory structure of a conventional errorcovariance matrix φ⁻¹(n). Referring to FIG. 1, the conventional errorcovariance matrix φ⁻¹(n), which is applied to a channel equalizer, has asymmetrical memory structure with respect to a diagonal line P₁, P₂, P₃and P₄.

[0034]FIG. 2 illustrates the memory structure of an error covariancematrix φ⁻¹(n) according to an exemplary embodiment of the presentinvention. Referring to FIG. 2, only the upper-right portion of a memoryof the error covariance matrix φ⁻¹(n), which is applied to a channelequalizer, with respect to a diagonal line P₁, P₂, P₃ and P₄ is used.For this reason, if the size of the memory of the conventional errorcovariance matrix φ⁻¹(n) is N², the size of the memory of the errorcovariance matrix φ⁻¹(n) according to the exemplary embodiments of thepresent invention will be about 0.5N²,

[0035] When the total amount of calculation of a conventional channelequalizer using the error covariance matrix of FIG. 1, shown inEquations IV, is 4N²+7N, the total amount of calculation of a channelequalizer using the error covariance matrix of FIG. 2, shown inEquations VI, is reduced to 1.5N²+7N.

[0036] An exemplary method of updating a tap coefficient of a channelequalizer and a circuit therefore, according to the present invention,to which Equation VI and the error covariance matrix of FIG. 2 areapplied, will be explained in more detail below.

[0037]FIG. 3 is a block diagram of a channel equalizer according to anexemplary embodiment of the present invention. As illustrated in FIG. 3,a filtering circuit 400 of the channel equalizer includes an M-tapforward filter 410, an N-tap feedback filter 420 and an adder 430. It isbelieved that the structure and operation of the illustrated filteringcircuit 400 will be well known to those skilled in the art and thatdetailed descriptions of the structure and operation are, therefore,unnecessary.

[0038] An exemplary circuit for updating a tap coefficient includes asubtracter 500, a decoder 510, a updating circuit 520, a determinationcircuit 540, a multiplexer 560, a training signal register 570 and aconvergence examining and comparing unit 590 (a “CEC unit”).

[0039] The M-tap forward filter 410 includes M filter cells (or taps)that are connected to one another in series. The M-tap forward filter410 stores input data y(n) in the M filter cells, multiplies therespective data y(n) by corresponding equalizer coefficients c(n), andoutputs the multiplication results to the adder 430.

[0040] The N-tap feedback filter 420 includes N filter cells (or taps)that are connected to one another in series. The N-tap feedback filter420 stores respective output values s*(n) of the equalizer having anupdated coefficient, i.e., signals output from the multiplexer 560, inthe respective N filter cells, multiplies the data stored in therespective filter cells by corresponding equalizer coefficients c(n),and outputs the multiplication result to the adder 430.

[0041] The adder 430 adds signals output from the M-tap forward filter410 and the N-tap feedback filter 420 together and outputs the additionresult, i.e., a signal y^(*T)(n)c(n−1), to the determination circuit 540and the subtracter 500. The determination circuit 540, which may be aslicer, determines a value of the signal y^(*T)(n)c(n−1) to a certainvalue and outputs the certain value to the decoder 510. The certainvalue corresponds to the output value s*(n) of the equalizer having anupdated coefficient, i.e., the equalized output value s*(n).

[0042] The multiplexer 560 outputs a training signal stored in thetraining signal register 570 or the signal s*(n) output from thedetermination circuit 540 to the N-tap feedback filter 420, a forwarderror correction (FEC) circuit (not shown) and the subtracter 500, inresponse to a control signal CNTR. The subtracter 500 subtracts thesignal y^(*T)(n)c(n−1), which is output from the adder 430, from thesignal s*(n) output from the multiplexer 560, and then outputs thesubtraction result, i.e., an error signal e(n), to the CEC unit 590 anda third multiplier 5307.

[0043] The CEC unit 590 receives a threshold of visibility TOV and theerror signal e(n) output from the subtracter 500, compares the thresholdof visibility TOV with a square of the error signal e(n), and outputsthe comparison result COMO to the decoder 510. The decoder 510 decodesthe control signal CNTR and the comparison result COMO and outputs thedecoding result EN/DEN to an error covariance register 5201, a Kalmangain register 5203, and a multiplexer 5211.

[0044] The updating circuit 520, which embodies the Kalman algorithm,includes the error covariance register 5201, the Kalman gain register5203, a Kalman gain updating unit 5205, a first multiplier 5207, asubtracter 5209, the multiplexer 5211, a second multiplier 5309, thethird multiplier 5307, an adder 5305, a coefficient updating register5303 and a data register 5313.

[0045] It is possible to perform the LMS algorithm using the secondmultiplier 5309, the third multiplier 5307, the adder 5305, thecoefficient updating register 5303 and the data register 5313. Asindicated by reference numeral 530, these components comprise a circuitfor performing the LMS algorithm.

[0046] The error covariance register 5201 stores an error covariancematrix φ⁻¹(n) and the Kalman gain register 5203 stores a Kalman gainK(n). The Kalman gain updating unit 5205 updates the Kalman gain K(n) inresponse to the Kalman gain K(n) output from the Kalman gain register5203, a signal φ⁻¹(n−1) output from the error covariance register 5201,and data y(n) output from the data register 5313, and then outputs theupdated Kalman gain K(n) to the Kalman gain register 5203.

[0047] The first multiplier 5207 receives the Kalman gain K(n) outputfrom the Kalman gain register 5203, the signal φ⁻¹(n−1) output from theerror covariance register 5201, and the data y(n) output from the dataregister 5313, multiplies them, and outputs the multiplication result tothe subtracter 5209. The subtracter 5209 subtracts a signal output fromthe first multiplier 5207 from the signal φ⁻¹(n−1) output from the errorcovariance register 5201, and outputs the subtraction result to theerror covariance register 5201.

[0048] The multiplexer 5211 outputs the Kalman gain K(n) output from theKalman gain register 5203 or a signal output from the second multiplier5309 to the third multiplier 5307, in response to the signal EN/DENoutput from the decoder 510. The second multiplier 5309 receives a stepsize μ and the data y(n) output from the data register 5313, multipliesthem, and outputs the multiplication result to the multiplexer 5211. Thethird multiplier 5307 receives the error signal e(n) output from thesubtracter 500 and a signal output from the multiplexer 5211, multipliesthem, and outputs the multiplication result to the adder 5305.

[0049] The adder 5305 receives a signal output from the third multiplier5307 and a signal c(n−1) output from the coefficient updating register5303, adds them together, and outputs the addition result to thecoefficient updating register 5303. The coefficient updating register5303 receives a signal output from the adder 5305, updates thecoefficient of the equalizer based on the received signal, and outputsan updated coefficient c(n) to the M-tap forward filter 410 and theN-tap feedback filter 420. The data register 5313 receives and storesthe input data y(n).

[0050] The operations of the error covariance register 5201 and theKalman gain register 5203, which depend on the decoding result EN/DENobtained by decoding the control signal CNRT and the signal COMO outputfrom the CEC unit 590, are illustrated in T1 below: T1 controlComparison Error signal result COMO Covariance Kalman Gain algorithmCNTR (e(n)² < TOV) Register Register used training 1 (convergence)inactivation inactivation LMS signal training 0 (divergence) activationactivation Kalman signal real 1 (convergence) inactivation inactivationLMS data real 0 (divergence) inactivation inactivation LMS data

[0051] As shown in T1, the comparison result COMO is 1 when the squareof the error e(n) is smaller than the threshold of visibility TOV and is0 when the square of the error e(n) is equal to or greater than thethreshold of visibility TOV.

[0052] The decoder 510 decodes the control signal CNTR and the signalCOMO output from the CEC unit 590 and determines whether the coefficientof the equalizer will be updated using the Kalman algorithm or the LMSalgorithm.

[0053] When the error covariance register 5201 and the Kalman gainregister 5203 are inactivated in response to the signal EN/DEN outputfrom the decoder 510, the multiplexer 5211 outputs the signal outputfrom the second multiplier 5309 to the third multiplier 5307 in order toupdate the coefficient of the equalizer using the LMS algorithm.

[0054] However, when the error covariance register 5201 and the Kalmangain register 5203 are activated in response to the signal EN/DEN outputfrom the decoder 510, the multiplexer 5211 outputs the signal K(n)output from the Kalman gain register 5203 to the third multiplier 5307in order to update the coefficient of the equalizer using the Kalmanalgorithm.

[0055]FIG. 4 is a flowchart illustrating a method of updating acoefficient of a channel equalizer according to an exemplary embodimentof the present invention. An exemplary method for updating a tapcoefficient according to an exemplary embodiment of the presentinvention will be explained below with reference to FIGS. 3 and 4.

[0056] As shown in Equations III and IV, a signal, i.e., an error e(n),output from the subtracter 500 is expressed as the difference between asignal y^(*T)(n)c(n−1) output from the adder 430 (or the equalizer) anda signal s*(n) output from the multiplexer 560. The signal s*(n) is atraining signal or a signal output from the determination circuit 540.

[0057] First, in step 210, the CEC unit 590 determines whether the errore(n) of the channel equalizer converges within the range of a thresholdof visibility TOV and outputs the determination result COMO. In detail,according to exemplary embodiments of the invention, the CEC unit 590determines whether the square of the error e(n) converges within therange of the threshold of visibility TOV, as illustrated in T1, andoutputs the determination result COMO.

[0058] If the square of the error e(n) is smaller than the threshold ofvisibility TOV, i.e., converges, a signal output from the CEC unit 590is activated, that is, the signal has a logic value of “1”. If, however,the error e(n) falls outside the range of the threshold of visibilityTOV, i.e., diverges, the error covariance register 5210 and the Kalmangain register 5203 are inactivated in response to signal EN/DEN outputfrom the decoder 510. In this embodiment, the multiplexer 5211 iscapable of outputting a signal output from the second multiplier 5309 tothe third multiplier 5307 in response to the signal EN/DEN output fromthe decoder 510.

[0059] When the error e(n) converges, i.e., falls within the range ofthe threshold of visibility TOV, the updating circuit 520 updates thetap coefficient of the channel equalizer using the LMS algorithm. Asillustrated in FIG. 3, the second multiplier 5309 multiplies the stepsize μ by a square of the data y(n) output from the data register 5313and outputs the multiplication result to the multiplexer 5211. Themultiplexer 5211 then outputs the signal output from the secondmultiplier 5307 to the third multiplier 5307 in response to the signalEN/DEN output from the decoder 510. The third multiplier 5307 multipliesa signal output from the multiplexer 5211 by the signal e(n) output fromthe subtracter 500 and outputs the multiplication result to the adder5305.

[0060] The adder 5305 adds the signal c(n−1) output from the coefficientupdating register 5303 and a signal output from the third multiplier5307 and outputs the addition result c(n) to the coefficient updatingregister 5303. However, when the error e(n) does not converge within therange of the threshold of visibility TOV, the channel equalizerdetermines whether an input control signal CNTR is the training signalor not in step 220. If the control signal CNTR is the training signal,the updating circuit 520 updates the tap coefficient of the channelequalizer using the Kalman algorithm in step 230.

[0061] Referring to T1 and FIG. 4, in response to the signal EN/DENoutput from the decoder 510, the error covariance register 5210 and theKalman gain register 5203 are activated and the multiplexer 5211 outputsa signal output from the Kalman gain register 5203 to the thirdmultiplier 5307. The third multiplier 5307 multiplies the signal K(n)output from the multiplexer 5211 by the signal e(n) output from thesubtracter 500 and outputs the multiplication result to the adder 5305.The adder 5305 adds the signal c(n−1) output from the coefficientupdating register 5303 and the signal output from the third multiplier5307 and outputs the addition result c(n) to the coefficient updatingregister 5303.

[0062] If the control signal CNTR is not the training signal (forexample, it is real data), the updating circuit 520 then updates the tapcoefficient of the channel equalizer using the LMS algorithm in step240.

[0063] After updating the coefficient of the equalizer using the LMSalgorithm or the Kalman algorithm, the determination circuit 540receives the signal y*T(n)c(n−1) output from the adder 430 anddetermines the signal y^(*T)(n)c(n−1) as a certain value in step 250.

[0064] As described above, in an exemplary method for updating a tapcoefficient of a channel equalizer and a circuit suitable for performingthe method according to the present invention, a tap coefficient isselectively updated using either the Kalman algorithm or the LMSalgorithm, thereby significantly reducing the amount of calculationrequired if only the Kalman algorithm was used while improving theperformance that could be achieved using only a LMS algorithm.

[0065] While this invention has been particularly shown and describedwith reference to preferred embodiments thereof, it will be understoodby those skilled in the art that various changes in form and details maybe made therein without departing from the spirit and scope of theinvention as defined by the appended claims.

What is claimed is:
 1. A method of updating a tap coefficient of achannel equalizer, comprising: determining whether or not an error ofthe channel equalizer converges within a range of a threshold ofvisibility; and updating the tap coefficient of the channel equalizerusing a least mean square (LMS) algorithm if the error converges withinthe range of the threshold of visibility or if the error does notconverge within the range of the threshold of visibility and a controlsignal is in a first state; or updating the tap coefficient of thechannel equalizer using a Kalman algorithm if the error does notconverge within the range of the threshold of visibility and the controlsignal is in a second state.
 2. A method of updating a tap coefficientof a channel equalizer according to claim 1, wherein: determiningwhether or not an error of the channel equalizer converges within arange of a threshold of visibility includes determining whether a squareof the error of the channel equalizer is smaller or larger than thethreshold of visibility.
 3. A method of updating a tap coefficient of achannel equalizer according to claim 1, wherein: the second state of thecontrol signal is a training signal.
 4. A method of updating a tapcoefficient of a channel equalizer according to claim 3, wherein: theerror is a difference between the training signal and a signal outputfrom the channel equalizer.
 5. A method of updating a tap coefficient ofa channel equalizer according to claim 1, wherein: the error is adifference between a channel equalizer output signal and a determinationcircuit output signal, wherein the determination circuit output signalhas a certain value corresponding to the channel equalizer outputsignal.
 6. A method of updating a tap coefficient of a channel equalizeraccording to claim 1, wherein: when the tap coefficient of the channelequalizer is updated using the LMS algorithm, the tap coefficient isupdated with the following equation: c(n)=c(n−1)+μe(n)y(n) and furtherwherein c(n) denotes an updated tap coefficient vector of the channelequalizer, c(n'1) denotes a tap coefficient vector of the channelequalizer that will be updated to obtain c(n), μ denotes a step size,e(n) denotes an error of the channel equalizer and y(n) denotes datainput to the channel equalizer.
 7. A method of updating a tapcoefficient of a channel equalizer according to claim 1, wherein: whenthe tap coefficient of the channel equalizer is updated using the Kalmanalgorithm, the coefficient is updated with the following equation:c(n)=c(n−1)+K(n)e(n) and further wherein c(n) denotes an updated tapcoefficient vector of the channel equalizer, c(n−1) denotes a tapcoefficient vector of the channel equalizer updated to obtain c(n), K(n)denotes a Kalman gain vector and e(n) denotes an error of the channelequalizer.
 8. A circuit for updating a tap coefficient of a channelequalizer comprising: a convergence examining and comparing (CEC) unitarranged and configured to determine if a received error of the channelequalizer converges within a range of a threshold of visibility; and anupdating circuit arranged and configured to update the tap coefficientusing a LMS algorithm when the error converges within the range of thethreshold of visibility or when the error does not converge within therange of the threshold of visibility and a control signal is in a firststate, and using a Kalman algorithm when the error does not convergewithin the range of the threshold of visibility and the control signalis in a second state.
 9. A circuit for updating a tap coefficient of achannel equalizer according to claim 8, wherein: the updating circuitupdates the tap coefficient of the channel equalizer using the Kalmanalgorithm only when the second state of the control signal is a trainingsignal.
 10. A circuit for updating a tap coefficient of a channelequalizer according to claim 8, wherein: the updating circuit isarranged and configured to update the tap coefficient of the channelequalizer using the LMS algorithm by executing an equationc(n)=c(n−1)+μe(n)y(n) wherein c(n) denotes an updated tap coefficientvector of the channel equalizer, c(n−1) denotes a tap coefficient vectorof the channel equalizer updated to obtain c(n), μ denotes the stepsize, e(n) denotes an error of the channel equalizer and y(n) denotesdata input to the channel equalizer.
 11. A circuit for updating a tapcoefficient of a channel equalizer according to claim 8, wherein: theupdating circuit is arranged and configured to update the tapcoefficient of the channel equalizer using the Kalman algorithm byexecuting an equation c(n)=c(n−1)+K(n)e(n) wherein c(n) denotes anupdated tap coefficient vector of the channel equalizer, c(n−1) denotesa tap coefficient vector of the channel equalizer that will be updatedto obtain c(n), K(n) denotes a Kalman gain vector and e(n) denotes anerror of the channel equalizer.
 12. A circuit for updating a tapcoefficient of a channel equalizer comprising: a channel equalizerarranged and configured to produce a channel equalizer output signal; aslicer arranged and configured to determine a certain valuecorresponding to the channel equalizer output signal and generate aslicer output signal corresponding to the certain value; a selectioncircuit arranged and configured to receive a control signal, the sliceroutput signal and a training signal, and, in response to the controlsignal, output the slicer output signal or the training signal as aselection circuit output signal; a subtracter arranged and configured tosubtract the channel equalizer output signal from the selection circuitoutput signal and generate an error output signal; a convergenceexamining and comparing (CEC) unit arranged and configured to compare arange of the threshold of visibility with the error output signal andgenerate a first CEC output signal when the error output signalconverges within the range of threshold of visibility or a second CECoutput signal when the error output signal does not converge within therange of the threshold of visibility; a decoder arranged and configuredto receive the control signal and the output signal of the CEC unit andproduce a decoder output signal; and an updating circuit arranged andconfigured to update the tap coefficient of the channel equalizer inresponse to the decoder output signal, wherein the updating circuitupdates the tap coefficient of the channel equalizer using a LMSalgorithm when the decoder output signal is in a first state and using aKalman algorithm when the decoder output signal is in a second state.13. A circuit for updating a tap coefficient of a channel equalizeraccording to claim 12, wherein: the decoder signal output is in thefirst state when the error output signal converges within a range of thethreshold of visibility or the control signal is in a first state; andthe decoder signal output is in the second state when the error outputsignal does not converge within a range of the threshold of visibilityand the control signal is in a second state.
 14. A circuit for updatinga tap coefficient of a channel equalizer according to claim 13, wherein:when the control signal is in the second state, the selection circuitoutput signal is the training signal.
 15. A circuit for updating a tapcoefficient of a channel equalizer according to claim 12, wherein: afirst portion of the updating circuit is arranged and configured toupdate the tap coefficient of the channel equalizer using the LMSalgorithm by executing an equation c(n)=c(n−1)+μe(n)y(n) and a secondportion of the updating circuit is arranged and configured to update thetap coefficient of the channel equalizer using the Kalman algorithm byexecuting an equation c(n)=c(n−1)+K(n)e(n) wherein c(n) denotes anupdated tap coefficient vector of the channel equalizer, c(n−1) denotesa tap coefficient vector of the channel equalizer that will be updatedto obtain c(n), μ denotes the step size, e(n) denotes an error of thechannel equalizer, y(n) denotes data input to the channel equalizer andK(n) denotes a Kalman gain vector.
 16. A circuit for updating a tapcoefficient of a channel equalizer comprising: a channel equalizerarranged and configured to produce a channel equalizer output signal;means for generating a determination signal corresponding a value of thechannel equalizer output signal; means for receiving a control signal,the determination signal and a training signal and selectivelyoutputting the determination signal or the training signal; means forgenerating an error signal; means for comparing the error signal to athreshold of visibility and generating a comparator output signal; andmeans for selectively updating the tap coefficient using a LMS algorithmor a Kalman algorithm based on a control signal state and a comparatoroutput signal state.
 17. A circuit for updating a tap coefficient of achannel equalizer according to claim 16, wherein: the means forgenerating the determination signal is a slicer; the means for receivingthe control signal, the determination signal and the training signal isa multiplexer; the means for generating the error signal is asubtracter; and the means for comparing the error signal to a thresholdof visibility and generating the comparator output signal is aconvergence examining and comparing unit.